论文标题
通过深层神经网络自组织的人为行动识别和评估
Human Action Recognition and Assessment via Deep Neural Network Self-Organization
论文作者
论文摘要
人类行为的强大识别和评估在人类机器人相互作用（HRI）领域至关重要。尽管最先进的动作感知模型在大规模的动作数据集中表现出显着的结果，但它们大多缺乏在自然的HRI场景中运行所需的灵活性，鲁棒性和可伸缩性，这需要连续获取感官信息，以及实时的分类或评估人体模式。在本章中，我介绍了一组分层模型，以通过使用神经网络自组织从深度图和RGB图像中学习和识别动作。这些模型的一个特殊性是使用不断增长的自组织网络，这些网络迅速适应非平稳分布并实施专门的机制，以从时间相关的输入中持续学习。
The robust recognition and assessment of human actions are crucial in human-robot interaction (HRI) domains. While state-of-the-art models of action perception show remarkable results in large-scale action datasets, they mostly lack the flexibility, robustness, and scalability needed to operate in natural HRI scenarios which require the continuous acquisition of sensory information as well as the classification or assessment of human body patterns in real time. In this chapter, I introduce a set of hierarchical models for the learning and recognition of actions from depth maps and RGB images through the use of neural network self-organization. A particularity of these models is the use of growing self-organizing networks that quickly adapt to non-stationary distributions and implement dedicated mechanisms for continual learning from temporally correlated input.
